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DBMS > Apache Druid vs. Citus vs. Vertica

System Properties Comparison Apache Druid vs. Citus vs. Vertica

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonCitus  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelRelational DBMS
Time Series DBMS
Relational DBMSRelational DBMS infoColumn oriented
Secondary database modelsDocument storeSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.18
Rank#84  Overall
#45  Relational DBMS
#7  Time Series DBMS
Score1.91
Rank#121  Overall
#57  Relational DBMS
Score9.78
Rank#42  Overall
#26  Relational DBMS
Websitedruid.apache.orgwww.citusdata.comwww.vertica.com
Technical documentationdruid.apache.org/­docs/­latest/­designdocs.citusdata.comvertica.com/­documentation
DeveloperApache Software Foundation and contributorsOpenText infopreviously Micro Focus and Hewlett Packard
Initial release201220102005
Current release30.0.0, June 20248.1, December 201812.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache license v2Open Source infoAGPL, commercial license also availablecommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaCC++
Server operating systemsLinux
OS X
Unix
LinuxLinux
Data schemeyes infoschema-less columns are supportedyesYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateyesyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.noyes infospecific XML type available, but no XML query functionalityno
Secondary indexesyesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLSQL for queryingyes infostandard, with numerous extensionsFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJDBC
RESTful HTTP/JSON API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnouser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.yes, PostgreSQL PL/pgSQL, with minor differences
Triggersnoyesyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesSource-replica replication infoother methods possible by using 3rd party extensionsMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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More resources
Apache DruidCitusVertica infoOpenText™ Vertica™
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